{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cache Dataset Tutorial and Speed Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to accelerate PyTorch medical DL program based on MONAI CacheDataset.  \n",
    "It's modified from the Spleen 3D segmentation tutorial notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from monai.data import Dataset, CacheDataset\n",
    "from monai.transforms import \\\n",
    "    Compose, LoadNiftid, AddChanneld, ScaleIntensityRanged, RandCropByPosNegLabeld, \\\n",
    "    RandAffined, Spacingd, Orientationd, ToTensord\n",
    "from monai.data import list_data_collate, sliding_window_inference\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import compute_meandice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = '/workspace/data/medical/Task09_Spleen'\n",
    "train_images = glob.glob(os.path.join(data_root, 'imagesTr', '*.nii.gz'))\n",
    "train_labels = glob.glob(os.path.join(data_root, 'labelsTr', '*.nii.gz'))\n",
    "data_dicts = [{'image': image_name, 'label': label_name}\n",
    "              for image_name, label_name in zip(train_images, train_labels)]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation\n",
    "  \n",
    "Deterministic transforms during training:  \n",
    "- LoadNiftid  \n",
    "- AddChanneld  \n",
    "- Spacingd  \n",
    "- Orientationd  \n",
    "- ScaleIntensityRanged  \n",
    "  \n",
    "Non-deterministic transforms:  \n",
    "- RandCropByPosNegLabeld  \n",
    "- ToTensord\n",
    "\n",
    "All the validation transforms are deterministic.  \n",
    "The results of all the deterministic transforms will be cached to accelerate training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transforms = Compose([\n",
    "    LoadNiftid(keys=['image', 'label']),\n",
    "    AddChanneld(keys=['image', 'label']),\n",
    "    Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), interp_order=(3, 0)),\n",
    "    Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
    "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "    # randomly crop out patch samples from big image based on pos / neg ratio\n",
    "    # the image centers of negative samples must be in valid image area\n",
    "    RandCropByPosNegLabeld(keys=['image', 'label'], label_key='label', size=(96, 96, 96), pos=1,\n",
    "                           neg=1, num_samples=4, image_key='image', image_threshold=0),\n",
    "    ToTensord(keys=['image', 'label'])\n",
    "])\n",
    "val_transforms = Compose([\n",
    "    LoadNiftid(keys=['image', 'label']),\n",
    "    AddChanneld(keys=['image', 'label']),\n",
    "    Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), interp_order=(3, 0)),\n",
    "    Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
    "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "    ToTensord(keys=['image', 'label'])\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_deterministic():\n",
    "    train_transforms.set_random_state(seed=0)\n",
    "    torch.manual_seed(0)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(train_ds, val_ds):\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, collate_fn=list_data_collate)\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(dimensions=3, in_channels=1, out_channels=2, channels=(16, 32, 64, 128, 256),\n",
    "                 strides=(2, 2, 2, 2), num_res_units=2, norm=Norm.BATCH).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, do_softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "\n",
    "    epoch_num = 600\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    epoch_loss_values = list()\n",
    "    metric_values = list()\n",
    "    epoch_times = list()\n",
    "    total_start = time.time()\n",
    "    for epoch in range(epoch_num):\n",
    "        epoch_start = time.time()\n",
    "        print('-' * 10)\n",
    "        print('Epoch {}/{}'.format(epoch + 1, epoch_num))\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = batch_data['image'].to(device), batch_data['label'].to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_function(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print('%d/%d, train_loss: %0.4f, step time: %0.4f' %\n",
    "                  (step, len(train_ds) // train_loader.batch_size, loss.item(), time.time() - step_start))\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print('epoch %d average loss: %0.4f' % (epoch + 1, epoch_loss))\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                metric_sum = 0.\n",
    "                metric_count = 0\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = val_data['image'].to(device), val_data['label'].to(device)\n",
    "                    roi_size = (160, 160, 160)\n",
    "                    sw_batch_size = 4\n",
    "                    val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                    value = compute_meandice(y_pred=val_outputs, y=val_labels, include_background=False,\n",
    "                                             to_onehot_y=True, mutually_exclusive=True)\n",
    "                    metric_count += len(value)\n",
    "                    metric_sum += value.sum().item()\n",
    "                metric = metric_sum / metric_count\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    torch.save(model.state_dict(), 'best_metric_model.pth')\n",
    "                    print('saved new best metric model')\n",
    "                print('current epoch %d current mean dice: %0.4f best mean dice: %0.4f at epoch %d'\n",
    "                      % (epoch + 1, metric, best_metric, best_metric_epoch))\n",
    "        print('time consuming of epoch %d is: %0.4f' % (epoch + 1, time.time() - epoch_start))\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "    print('train completed, best_metric: %0.4f  at epoch: %d, total time: %0.4f' %\n",
    "          (best_metric, best_metric_epoch, time.time() - total_start))\n",
    "    return epoch_num, time.time() - total_start, epoch_loss_values, metric_values, epoch_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define regular Dataset for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_deterministic()\n",
    "train_ds = Dataset(data=train_files, transform=train_transforms)\n",
    "val_ds = Dataset(data=val_files, transform=val_transforms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train with regular Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_num, total_time, epoch_loss_values, metric_values, epoch_times = train_process(train_ds, val_ds)\n",
    "print('Total training time of %d epochs with normal Dataset: %0.4f' % (epoch_num, total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Cache Dataset for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data to cache and executing transforms...\n",
      "32/32 [==============================]  \n",
      "Loading data to cache and executing transforms...\n",
      "9/9 [==============================]  \n"
     ]
    }
   ],
   "source": [
    "set_deterministic()\n",
    "cache_init_start = time.time()\n",
    "cache_train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0)\n",
    "cache_val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0)\n",
    "cache_init_time = time.time() - cache_init_start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train with Cache Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_num, cache_total_time, cache_epoch_loss_values, cache_metric_values, cache_epoch_times = \\\n",
    "    train_process(cache_train_ds, cache_val_ds)\n",
    "print('Total training time of %d epochs with CacheDataset: %0.4f' % (epoch_num, cache_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure('train', (12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Regular Epoch Average Loss')\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='red')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('Regular Val Mean Dice')\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='red')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title('Cache Epoch Average Loss')\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='green')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title('Cache Val Mean Dice')\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure('train', (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Total Train Time(600 epochs)')\n",
    "plt.bar('regular', total_time, 1, label='Regular Dataset', color='red')\n",
    "plt.bar('cache', cache_init_time + cache_total_time, 1, label='Cache Dataset', color='green')\n",
    "plt.bar('cache', cache_init_time, 1, label='Cache Init', color='orange')\n",
    "plt.ylabel('secs')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Epoch Time')\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('secs')\n",
    "plt.plot(x, epoch_times, label='Regular Dataset', color='red')\n",
    "plt.plot(x, cache_epoch_times, label='Cache Dataset', color='green')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
